Learning method, storage medium storing learning program, and information processing device

Abstract

A learning method is executed by a computer. The method includes: obtaining a trained model in which training data having non-linear characteristics is learned by supervised learning using a first teacher label; classifying the training data by using the obtained trained model and calculating a score related to a factor of the obtainment of the classification result for the training data; clustering the training data based on the calculated score; applying a second teacher label based on clusters obtained from the clustering to the training data; and executing supervised learning of a decision tree by using the training data and the applied second teacher label.

What is claimed is: 
     
         1 . A computer-implemented machine learning method comprising:
 obtaining a machine learning model which has learned training data having non-linear characteristics by supervised learning;   classifying the training data by using the obtained machine learning model and calculating a score related to a factor of a classification result of the classifying;   clustering the training data based on the calculated score;   labeling the training data with a first label based on a cluster generated by the clustering; and   executing supervised learning of a decision tree by using the labeled training data.   
     
     
         2 . The learning method according to  claim 1 , wherein the clustering includes:
 deleting training data of smallest degree of influence on an error from the training data based on errors of the training data in a case of a classification using the training data having closer scores to determine representative data representing the clusters; and   clustering the training data based on the scores and the representative data.   
     
     
         3 . The learning method according to  claim 1 , wherein
 the labeling includes changing a second label of the training data, used when the machine learning model has learned the training data, to the first label; and   the executing the supervised learning of the decision tree includes replacing a node associated with the first label included in the learned decision tree with a node associated with the second label based on a correspondence relationship in the changing from the second label to the first label.   
     
     
         4 . A non-transitory computer-readable storage medium having stored a learning program causing a computer to execute a process comprising:
 obtaining a machine learning model which has learned training data having non-linear characteristics by supervised learning;   classifying the training data by using the obtained machine learning model and calculating a score related to a factor of a classification result of the classifying;   clustering the training data based on the calculated score;   labeling the training data with a first label based on a cluster generated by the clustering; and   executing supervised learning of a decision tree by using the labeled training data.   
     
     
         5 . The storage medium according to  claim 4 , wherein the clustering includes:
 deleting training data of smallest degree of influence on an error from the training data based on errors of the training data in a case of a classification using the training data having closer scores to determine representative data representing the clusters; and   clustering the training data based on the scores and the representative data.   
     
     
         6 . The storage medium according to  claim 4 , wherein
 the labeling includes changing a second label of the training data, used when the machine learning model has learned the training data, to the first label; and   the executing the supervised learning of the decision tree includes replacing a node associated with the first label included in the learned decision tree with a node associated with the second label based on a correspondence relationship in the changing from the second label to the first label.   
     
     
         7 . An information processing device comprising:
 a memory, and   a processor coupled to the memory and configured to:
 obtain a machine learning model which has learned training data having non-linear characteristics by supervised learning; 
 classify the training data by using the obtained machine learning model and calculate a score related to a factor of a classification result of the classifying; 
 cluster the training data based on the calculated score; 
 label the training data with a first label based on a cluster generated; and 
 execute supervised learning of a decision tree by using the labeled training data. 
   
     
     
         8 . The information processing device according to  claim 7 , wherein the processor is configured to cluster the training data by at least,
 deleting training data of smallest degree of influence on an error from the training data based on errors of the training data in a case of a classification using the training data having closer scores to determine representative data representing the clusters; and   clustering the training data based on the scores and the representative data.   
     
     
         9 . The information processing device according to  claim 7 , wherein
 the processor is configured to label the training data with the first label by changing a second label of the training data, used when the machine learning model has learned the training data, to the first label; and   the processor is configured to execute the supervised learning of the decision tree by replacing a node associated with the first label included in the learned decision tree with a node associated with the second label based on a correspondence relationship in the changing from the second label to the first label.